Thalamic nuclei segmentation from T1-weighted MRI: unifying and benchmarking state-of-the-art methods

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Williams, B. ORCID: https://orcid.org/0000-0003-3844-3117, Nguyen, D., Vidal, J. P. and Saranathan, M. (2024) Thalamic nuclei segmentation from T1-weighted MRI: unifying and benchmarking state-of-the-art methods. Imaging Neuroscience, 2. pp. 1-16. ISSN 2837-6056

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To link to this item DOI: 10.1162/imag_a_00166

Abstract/Summary

The thalamus and its constituent nuclei are critical for a broad range of cognitive, linguistic, and sensorimotor processes, and are implicated in many neurological and neurodegenerative conditions. However, the functional involvement and specificity of thalamic nuclei in human neuroimaging work is underappreciated and not well studied due, in part, to technical challenges of accurately identifying and segmenting nuclei. This challenge is further exacerbated by a lack of common nomenclature for comparing segmentation methods. Here, we use data from healthy young (Human Connectome Project, n=100) and older healthy adults, plus those with mild cognitive impairment and Alzheimer’s disease (Alzheimer’s Disease Neuroimaging Initiative, n=540), to benchmark four state of the art thalamic segmentation methods for T1 MRI (FreeSurfer, HIPS-THOMAS, SCS-CNN, and T1-THOMAS) under a single segmentation framework. Segmentations were compared using overlap and dissimilarity metrics to the Morel stereotaxic atlas, a widely accepted thalamic atlas. We also quantified each method’s estimation of thalamic nuclear degeneration across Alzheimer's disease progression, and how accurately early and late mild cognitive impairment, and Alzheimer’s disease could be distinguished from healthy controls. We show that the HIPS-THOMAS approach produced the most effective segmentations of individual thalamic nuclei relative to the Morel atlas, and was also most accurate in discriminating healthy controls from those with mild cognitive impairment and Alzheimer’s disease using individual nucleus volumes. This latter result was different when using whole thalamus volumes, where SCS-CNN approach was most accurate in classifying healthy controls. This work is the first to systematically compare the efficacy of anatomical thalamic segmentation approaches under a unified nomenclature. We also provide recommendations of which segmentation method to use for studying the functional relevance of specific thalamic nuclei, based on their overlap and dissimilarity with the Morel atlas.

Item Type:	Article
Refereed:	Yes
Divisions:	Interdisciplinary Research Centres (IDRCs) > Centre for Integrative Neuroscience and Neurodynamics (CINN) Life Sciences > School of Psychology and Clinical Language Sciences > Ageing Life Sciences > School of Psychology and Clinical Language Sciences > Department of Psychology Life Sciences > School of Psychology and Clinical Language Sciences > Neuroscience
ID Code:	116049
Uncontrolled Keywords:	Thalamus, Segmentation, FreeSurfer, THOMAS, Human Connectome Project, Alzheimer's Disease
Publisher:	MIT Press

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Thalamic nuclei segmentation from T1-weighted MRI: unifying and benchmarking state-of-the-art methods

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